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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

The Application of Structural Reliability Techniques to Plume Impingement Loading of the Space Station Freedom Photovoltaic Array

A new aerospace application of structural reliability techniques is presented, where the applied forces depend on many probabilistic variables. This application is the plume impingement loading of the Space Station Freedom Photovoltaic Arrays. When the space shuttle berths with Space Station Freedom it must brake and maneuver towards the berthing point using its primary jets. The jet exhaust, or plume, may cause high loads on the photovoltaic arrays. The many parameters governing this problem are highly uncertain and random. An approach, using techniques from structural reliability, as opposed to the accepted deterministic methods, is presented which assesses the probability of failure of the array mast due to plume impingement loading. A Monte Carlo simulation of the berthing approach is used to determine the probability distribution of the loading. A probability distribution is also determined for the strength of the array. Structural reliability techniques are then used to assess the array mast design. These techniques are found to be superior to the standard deterministic dynamic transient analysis, for this class of problem. The results show that the probability of failure of the current array mast design, during its 15 year life, is minute.

Yunis, Isam S.↗
DOE OSTI2026

Neutrino-Argon Cross Sections in MicroBooNE: Measurements Spanning Multiple Interaction Channels, Final States, and Neutrino Fluxes

Neutrinos are one of the most elusive particles in the Standard Model of particle physics due to their tiny interaction cross section, which makes them challenging to detect and study. There are three known flavors of neutrinos, and any given neutrino probabilistically oscillates between them as a function of the particle's energy and propagation distance. Experimental characterization of these oscillations elucidates fundamental properties of the neutrino and the Standard Model. Meeting the precision goals of ongoing and future oscillation measurements requires detailed modeling of the way neutrinos interact with nuclear matter. Precision modeling of these interactions is a challenging theoretical problem, rich with intricate physics effects to explore, and requires input from equally precise measurements of neutrino-nucleus interaction cross sections spanning a broad range of scattering channels. To fill this need, there is an ongoing multi-experiment effort to measure these cross sections across energies, interaction channels, and nuclear targets. This thesis describes three analyses reporting neutrino-argon cross section measurements with data from the MicroBooNE liquid argon time projection chamber detector. These span multiple interaction channels, final state topologies, and neutrino fluxes. The first analysis is a set of inclusive charged current muon neutrino cross section measurements for final states with and without protons, which provides a unique view of the hadronic final state produced in these interactions. Second is a set of cross section measurements for neutral current neutral pion production, which provides a vital dataset on this under-characterized channel. Third is significant progress on measuring neutrinos produced by kaons decaying at rest, which represents a unique opportunity to measure cross sections with a mono-energetic flux of neutrinos. These measurements are accompanied by a modeling study in the GiBUU theory framework, which probes the sensitivity of the muon neutrino and pion production measurements to the modeling of nucleon-nucleon final state interactions in neutrino-nucleus scattering.

Bogart, Benjamin [Michigan U.]↗
NASA NTRS2010

Analysis of a Hypergolic Propellant Explosion During Processing of Launch Vehicles in the VAB

NASA is developing launch vehicles to support missions to Low Earth Orbit (LEO), the moon and deep space. Whether manned or unmanned, the vehicle components will likely be integrated in the Vehicle Assembly Building (VAB) at Kennedy Space Center (KSC) and typically include a fueled spacecraft (SC) that sits on top of one or more stages. The processing of a fueled SC involves hazardous operations when it is brought into the VAB Transfer Aisle and lifted a significant height for mating with lower stages. Accidents resulting from these hazardous operations could impact unrelated personnel working in buildings adjacent to the VAB. Safe separation distances based on the DOD Explosives Standards Quantity-Distance (Q-D) approach result in large IBD arcs. This paper presents site-specific air blast and fragmentation hazard analyses for comparison with the Q-D arcs as well as consequence and risk analyses to provide added information for the decision maker. A new physics-based fragmentation model is presented that includes: a) the development of a primary fragment list (which defines the fragment characteristics) associated with a hypergolic propellant explosion, b) a description of a 3D fragment bounce model, c) the results of probabilistic Monte-Carlo simulations (that include uncertainties in the fragment characteristics) to determine: i) the hazardous fragment density distance, ii) the expected number of wall/roof impacts and penetrations to over 40 buildings adjacent to the VAB, and iii) the risk to building occupants.

Chrostowski, Jon D.↗
NASA NTRS2004

Methodolgy For Evaluation Of Technology Impacts In Space Electric Power Systems

The Analysis and Management branch of the Power and Propulsion Office at NASA Glenn Research Center is responsible for performing complex analyses of the space power and In-Space propulsion products developed by GRC. This work quantifies the benefits of the advanced technologies to support on-going advocacy efforts. The Power and Propulsion Office is committed to understanding how the advancement in space technologies could benefit future NASA missions. They support many diverse projects and missions throughout NASA as well as industry and academia. The area of work that we are concentrating on is space technology investment strategies. Our goal is to develop a Monte-Carlo based tool to investigate technology impacts in space electric power systems. The framework is being developed at this stage, which will be used to set up a computer simulation of a space electric power system (EPS). The outcome is expected to be a probabilistic assessment of critical technologies and potential development issues. We are developing methods for integrating existing spreadsheet-based tools into the simulation tool. Also, work is being done on defining interface protocols to enable rapid integration of future tools. Monte Carlo-based simulation programs for statistical modeling of the EPS Model. I decided to learn and evaluate Palisade's @Risk and Risk Optimizer software, and utilize it's capabilities for the Electric Power System (EPS) model. I also looked at similar software packages (JMP, SPSS, Crystal Ball, VenSim, Analytica) available from other suppliers and evaluated them. The second task was to develop the framework for the tool, in which we had to define technology characteristics using weighing factors and probability distributions. Also we had to define the simulation space and add hard and soft constraints to the model. The third task is to incorporate (preliminary) cost factors into the model. A final task is developing a cross-platform solution of this framework.

Holda, Julie↗
NASA NTRS2013

Fracture Analysis of Particulate Reinforced Metal Matrix Composites

A fracture analysis of highly loaded particulate reinforced composites was performed using laser moire interferometry to measure the displacements within the plastic zone at the tip of an advancing crack. Ten castings were made of five different particulate reinforcement-aluminum alloy combinations. Each casting included net-shape specimens which were used for the evaluation of fracture toughness, tensile properties, and flexure properties resulting in an extensive materials properties data. Measured fracture toughness range from 14.1 MPa for an alumina reinforced 356 aluminum alloy to 23.9 MPa for a silicon carbide reinforced 2214 aluminum alloy. For the combination of these K(sub Ic) values and the measured tensile strengths, the compact tension specimens were too thin to yield true plane strain K(sub Ic) values. All materials exhibited brittle behavior characterized by very small tensile ductility suggesting that successful application of these materials requires that the design stresses be below the elastic limit. Probabilistic design principles similar to those used with ceramics are recommended when using these materials. Such principles would include the use of experimentally determined design allowables. In the absence of thorough testing, a design allowable stress of 60 percent of the measured ultimate tensile stress is recommended.

Min, James B.↗
DOE OSTI2021

$\mathrm{PPT}$-Multicore: performance prediction of Open$\mathrm{MP}$ applications using reuse profiles and analytical modeling

In this report we present PPT-Multicore, an analytical model embedded in the Performance Prediction Toolkit (PPT) to predict parallel applications’ performance running on a multicore processor. PPT-Multicore builds upon our previous work towards a multicore cache model. We extract LLVM basic block labeled memory trace using an architecture-independent LLVM-based instrumentation tool only once in an application’s lifetime. The model uses the memory trace and other parameters from an instrumented sequentially executed binary. We use probabilistic and computationally efficient reuse profiles to predict the cache hit rates and runtimes of OpenMP programs’ parallel sections. We model Intel’s Broadwell, Haswell, and AMD’s Zen2 architectures and validate our framework using different applications from PolyBench and PARSEC benchmark suites. The results show that PPT-Multicore can predict cache hit rates with an overall average error rate of 1.23% while predicting the runtime with an error rate of 9.08%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Massively Parallel Bayesian Model Calibration and Uncertainty Quantification with Applications to Nuclear Fuels and Materials

The U.S. Department of Energy (DOE)’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel cycle systems. This program has been providing engineering-scale support for the development of BISON, a high-fidelity and high-resolution fuel performance tool. Fuel behavior in a nuclear reactor is governed by a complex network of mechanisms interacting with various other physics aspects in the reactor system. Any model developed to represent the fuel behavior will likely be idealized resulting in uncertainties in their predictions compared to the observed data. As such, this report was motivated by the need to identify the sources of uncertainties and quantify and propagate them through the fuel model outputs. Such quantification of uncertainties will establish a level of model trustworthiness, identify approaches to improve the model trustworthiness, and even guide optimal experiment design for maximal information gain. To accomplish the uncertainty quantification for computational models, this report has relied on the Bayesian framework which provides probabilistic treatment of models their inputs and outputs. The current state-of-the-art on performing Bayesian Uncertainty Quantification (UQ) for nuclear engineering models using High Performance Computing (HPC) resources have been reviewed. Implementation of capabilities for massively parallel Bayesian UQ in Multiphysics Object-Oriented Simulation Environment (MOOSE) is discussed. Several verification cases are discussed to verify the accuracy of the quantified uncertainties using the developed computational capabilities in MOOSE. Then, the problem of quantifying the uncertainties in TRI-Structural isOtropic (TRISO) fuel silver release is addressed. For the first time, the uncertainties arising from the TRISO Fission Gas Release (FGR) model due to model inadequacy and experimental noise are quantified. Also, the Bayesian capabilities are applied to the calibration of the MATPRO creep model, a widely used model in several fuel assessment cases. The impact of the prediction uncertainties in the MATPRO model on the fuel cladding behavior as part of the TRIBULATION assessment case (which is an integral effects case) is investigated. This report concludes with a discussion on the future work for the UQ for computational models.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Cost- and Risk-Based Seismic Design Optimization of Nuclear Power Plant Safety Systems

Seismic analysis, design, and qualification of systems, structures, and components (SSCs) is a significant contributor to the capital cost of a nuclear power plant. To reduce capital costs of advanced nuclear power plants and make commercial nuclear energy more competitive, innovations are needed in their structural design and construction, and not just in the reactor core and associated systems. Seismic isolation has been identified as an important cost-cutting technology that enables standardization of equipment across various sites. This paper develops and demonstrates a cost- and risk-based seismic design optimization of a representative safety system in a nuclear power plant with the dual goals of minimizing overnight capital cost and meeting safety goals. The design optimization can also include component seismic isolation, in which case, the optimized design includes a set of equipment that needs to be seismically isolated to minimize capital cost. The open-source codes MASTODON and Dakota are used for seismic probabilistic risk assessment and design optimization, respectively. A generic nuclear facility with a safety system comprising SSCs that are common to nuclear power plants is considered for the demonstration of the design optimization and is assumed to be located at the Idaho National Laboratory site. Generic costs and seismic design cost functions are assumed for the SSCs of the safety system. The sum of the costs of the SSCs is minimized in the optimization process, while the risk of failure of the safety system is provided as a constraint. Furthermore, results show that the optimization process reduces capital costs significantly while automatically prioritizing the safety of SSCs that contribute most to the risk of the safety system.

42 ENGINEERING↗
NASA NTRSDate not supplied

Determining the Most Influencing Medical Conditions in MEDPRAT’s SIN Directed Graph

INTRODUCTION: The Susceptibility Inference Network (SIN) is a network of medical conditions, part of the Medical Extensible Probabilistic Risk Assessment Tool (MEDPRAT) developed by NASA to assess human health and medical risk to space exploration missions. The SIN is subject matter expert informed and acts as a prototype that provides relationships and dependencies between events modeled by MEDPRAT. Each vertex in the SIN has a weight which evaluates the severity of having the condition regardless of the progression from or to that condition. In this presentation, we consider two statistics to measure that stand alone risk: Quality Time Lost (QTL) and Loss of Crew Life (LOCL). Our goal is to identify the medical conditions that contribute the most to crew members QTL and LOCL risks due to progression of conditions in the network. We investigate how different computation parameters result in different condition rankings and address the choice of parameters that allows appropriate interventions to ensure space mission success. METHODS: The Katz score, one of many centrality measures created for ranking purposes in network analysis, takes into account all possible walks through the network, penalizing each additional step in a walk by a factor α called the Katz parameter. The literature does not provide specific values for the choice of α. We derive an analytical relationship between α and the maximum path length which has influence on the Katz score and ranking. Based on the probability of progression of each condition in the SIN, we identify that maximum path length of interest and calculate α that is then used in the Katz formula to rank the conditions in the SIN. RESULTS AND CONCLUSION: The effective probabilities of the SIN matrix generally fall below 10−6, which is below the level of the least influencing condition in the set. This corresponds to the probability of at most six consecutive progressions of a condition. Consequently, we calculate the Katz Parameter α and get 0.32. We rank the medical conditions and find that Acute Radiation Symptom is the condition the most prone to contribute to quality time loss due to progression.

risk analysis↗
DOE OSTI2026

Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events

This paper is the basis for a presentation help at the 2026 Georgia Tech Fault & Disturbance Analysis Conference, which can be found at OSTI # 3168287 Paper Abstract—Phasor Measurement Units (PMUs) stream time synchronized, high-resolution measurements from the grid, enabling data-driven techniques for event detection and classification. Accurate event classification improves grid reliability and stability. Events can be detected by varying numbers of PMUs and exhibit different durations depending on the event type. This variability challenges standard classifiers that require uniform input sizes. Moreover, multiple events may coincide, which increases classification complexity. Standard classifiers assign each instance to the class with the highest predicted probability, whereas overlapping events may exhibit comparable probabilities across multiple classes. In this study, to handle data size variability, we extract a wide range of time–frequency domain features from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, LightGBM, Support Vector Machine, and Multilayer Perceptron. To account for overlapping events, a probabilistic post-processing step is applied. For a given data instance, if multiple predicted class probabilities exceed 30% and the differences between them are less than 10%, the event is assigned to multiple classes. Experiments using real-world PMU data demonstrate that the Random Forest and XGBoost models achieve the highest accuracy, while the proposed post-processing method yields perfect classification performance on external unseen test sets.

Nematirad, Reza [Danovo Energy Solutions]↗
DOE OSTI2026

Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events

Danovo Energy Solution's presented its paper named: Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events at the 2026 Georgia Tech Fault & Disturbance Analysis Conference. The full paper can be found at OSTI ID# 3169150 Paper Abstract—Phasor Measurement Units (PMUs) stream time synchronized, high-resolution measurements from the grid, enabling data-driven techniques for event detection and classification. Accurate event classification improves grid reliability and stability. Events can be detected by varying numbers of PMUs and exhibit different durations depending on the event type. This variability challenges standard classifiers that require uniform input sizes. Moreover, multiple events may coincide, which increases classification complexity. Standard classifiers assign each instance to the class with the highest predicted probability, whereas overlapping events may exhibit comparable probabilities across multiple classes. In this study, to handle data size variability, we extract a wide range of time–frequency domain features from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, LightGBM, Support Vector Machine, and Multilayer Perceptron. To account for overlapping events, a probabilistic post-processing step is applied. For a given data instance, if multiple predicted class probabilities exceed 30% and the differences between them are less than 10%, the event is assigned to multiple classes. Experiments using real-world PMU data demonstrate that the Random Forest and XGBoost models achieve the highest accuracy, while the proposed post-processing method yields perfect classification performance on external unseen test sets.

Nematirad, Reza [Danova Energy Solutions]↗
NASA NTRS2007

Stress Rupture Life Reliability Measures for Composite Overwrapped Pressure Vessels

Composite Overwrapped Pressure Vessels (COPVs) are often used for storing pressurant gases onboard spacecraft. Kevlar (DuPont), glass, carbon and other more recent fibers have all been used as overwraps. Due to the fact that overwraps are subjected to sustained loads for an extended period during a mission, stress rupture failure is a major concern. It is therefore important to ascertain the reliability of these vessels by analysis, since the testing of each flight design cannot be completed on a practical time scale. The present paper examines specifically a Weibull statistics based stress rupture model and considers the various uncertainties associated with the model parameters. The paper also examines several reliability estimate measures that would be of use for the purpose of recertification and for qualifying flight worthiness of these vessels. Specifically, deterministic values for a point estimate, mean estimate and 90/95 percent confidence estimates of the reliability are all examined for a typical flight quality vessel under constant stress. The mean and the 90/95 percent confidence estimates are computed using Monte-Carlo simulation techniques by assuming distribution statistics of model parameters based also on simulation and on the available data, especially the sample sizes represented in the data. The data for the stress rupture model are obtained from the Lawrence Livermore National Laboratories (LLNL) stress rupture testing program, carried out for the past 35 years. Deterministic as well as probabilistic sensitivities are examined.

Murthy, Pappu L. N.↗
DOE OSTI2020

A Multi-Criteria Impact Evaluation Methodology Applied to FLEX Strategies in Nuclear Power Plants

As responses to Fukushima Dai-ichi nuclear accident, several advanced, near-term technologies have been or are being introduced to enhance safety of operating nuclear power plants (NPPs) as well as improving their sustainability. Examples include Accident-Tolerant Fuel designs, Diverse and Flexible Coping Strategies (FLEX), and passive cooling system designs. The combinations of these technologies, coupled with enhancements to plant components and systems and improved fuel cycle efficiency, constitute a concept of Enhanced Resilient Plant Systems, which is proposed and being explored by Idaho National Laboratory (INL) under the Risk-Informed Systems Analysis Pathway of the U.S. Department of Energy’s Light Water Reactor Sustainability Program. To decide if introducing a technology is necessary or worthy, the impacts of this technology on NPPs should be evaluated, preferably in a quantitative manner, and provided to decision makers. This paper summarizes an on-going work conducted by the authors on developing a multi-criteria impact evaluation methodology. This methodology is expected to be applicable to evaluate a variety of technologies, while FLEX strategy is adopted as a case study for this paper.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2006

Hard and Soft Constraints in Reliability-Based Design Optimization

This paper proposes a framework for the analysis and design optimization of models subject to parametric uncertainty where design requirements in the form of inequality constraints are present. Emphasis is given to uncertainty models prescribed by norm bounded perturbations from a nominal parameter value and by sets of componentwise bounded uncertain variables. These models, which often arise in engineering problems, allow for a sharp mathematical manipulation. Constraints can be implemented in the hard sense, i.e., constraints must be satisfied for all parameter realizations in the uncertainty model, and in the soft sense, i.e., constraints can be violated by some realizations of the uncertain parameter. In regard to hard constraints, this methodology allows (i) to determine if a hard constraint can be satisfied for a given uncertainty model and constraint structure, (ii) to generate conclusive, formally verifiable reliability assessments that allow for unprejudiced comparisons of competing design alternatives and (iii) to identify the critical combination of uncertain parameters leading to constraint violations. In regard to soft constraints, the methodology allows the designer (i) to use probabilistic uncertainty models, (ii) to calculate upper bounds to the probability of constraint violation, and (iii) to efficiently estimate failure probabilities via a hybrid method. This method integrates the upper bounds, for which closed form expressions are derived, along with conditional sampling. In addition, an l(sub infinity) formulation for the efficient manipulation of hyper-rectangular sets is also proposed.

Crespo, L.uis G.↗
NASA NTRS2023

A Confidence-Based Approach to Including Survivors in a Probabilistic TID Failure Assessment

A probabilistic total ionizing dose (TID) failure assessment is extended to include survivor data, enabling the bounding of failure probability to a desired confidence level (CL) without failure data. The extension provides an avenue for analyzing microelectronics tested for TID without reaching a failure mode, a scenario often encountered by missions utilizing commercial-off-the-shelf (COTS) technologies. Using the type-I censored likelihood formulation and a realistic upper bound on expected device performance, the failure probability space is bounded by confidence contours within the context of a variable environment. The framework accommodates any type of distribution assumed for the part failure or the environment under consideration. Furthermore, the framework can be utilized pre-emptively to plan future device TID tests, minimizing costs while meeting survival requirements. Heritage data may also be used as survivors to further minimize testing costs when parts are from the same lot, but the amount of constraint derived from heritage is limited. Altogether, the framework enables a formal, mathematically rigorous analysis of radiation tolerant devices tested to a maximum dose, as well as flight heritage, in a hardness assurance methodology.

confidence↗
DOE OSTI2024

Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models

Purpose To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET)'s SimCRC, MISCAN-Colon, and CRC-SPIN simulation models of the natural history colorectal cancer (CRC) with an emulator-based Bayesian algorithm and internally validate the model-predicted outcomes to calibration targets.Methods We used Latin hypercube sampling to sample up to 50,000 parameter sets for each CISNET-CRC model and generated the corresponding outputs. We trained multilayer perceptron artificial neural networks (ANNs) as emulators using the input and output samples for each CISNET-CRC model. We selected ANN structures with corresponding hyperparameters (i.e., number of hidden layers, nodes, activation functions, epochs, and optimizer) that minimize the predicted mean square error on the validation sample. We implemented the ANN emulators in a probabilistic programming language and calibrated the input parameters with Hamiltonian Monte Carlo-based algorithms to obtain the joint posterior distributions of the CISNET-CRC models' parameters. We internally validated each calibrated emulator by comparing the model-predicted posterior outputs against the calibration targets.Results The optimal ANN for SimCRC had 4 hidden layers and 360 hidden nodes, MISCAN-Colon had 4 hidden layers and 114 hidden nodes, and CRC-SPIN had 1 hidden layer and 140 hidden nodes. The total time for training and calibrating the emulators was 7.3, 4.0, and 0.66 h for SimCRC, MISCAN-Colon, and CRC-SPIN, respectively. The mean of the model-predicted outputs fell within the 95% confidence intervals of the calibration targets in 98 of 110 for SimCRC, 65 of 93 for MISCAN, and 31 of 41 targets for CRC-SPIN.Conclusions Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis, such as the CISNET CRC models. In this work, we present a step-by-step guide to constructing emulators for calibrating 3 realistic CRC individual-level models using a Bayesian approach.

artificial neural networks↗
DOE OSTI2022

Composite Power System Reliability with Renewables and Customer Flexibility

Composite Power System Reliability is defined as the computational procedure that quantifies the probability that the power system will perform the function of delivering electric power to customers adequately, on a continuous basis and with an acceptable quality. This definition leaves many details undefined and exemplifies the ambiguity in reliability analysis. The increasing deployment of wind and PV creates additional uncertainties that make reliability analysis a rather complex issue. Because of increased uncertainty the need for composite reliability analysis and utilization of results in power system planning is critical. New approaches are emerging for dealing with these problems from the operational point of view, including demand response programs, tapping on customer and distributed resource flexibility and new control approaches. The key question to be addressed is: how the new operational paradigms affect composite power system reliability. Here, this paper presents the ongoing work of the IEEE Composite System Reliability Task Force of the IEEE PES Reliability, Risk, Probability Application (RRPA) Subcommittee.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Stress-controlled fatigue of HfNbTaTiZr high-entropy alloy and associated deformation and fracture mechanisms

The stress-controlled fatigue tests are carried out at a stress ratio of 0.1 and a frequency of 10 Hz, and span both low-cycle and high-cycle regimes by varying the applied stress amplitudes. The high-cycle fatigue regime gives a fatigue strength of 497 MPa and a fatigue ratio of 0.44. At equivalent conditions, the alloy's fatigue strength is greater than all other high-entropy alloys (HEAs) with reported high-cycle fatigue data, dilute body-centered cubic alloys, and many structural alloys such as steels, titanium alloys, and aluminum alloys. Through in-depth analyses of crack-propagation trajectories, fracture-surface morphologies and deformation plasticity by means of various microstructural analysis techniques and theoretical frameworks, the alloy's remarkable fatigue resistance is attributed to delayed crack initiation in the high-cycle regime, which is achieved by retarding the formation of localized persistent slip bands, and its good resistance to crack propagation in the low-cycle regime, which is accomplished by intrinsic toughening backed up by extrinsic toughening. Additionally, the stochastic nature of the fatigue data is neatly captured with a 2-parameter Weibull model.

36 MATERIALS SCIENCE↗